title: Machine learning approach to an otoneurological classification problem.
abstract: In this paper we applied altogether 13 classification methods to otoneurological disease classification. The main point was to use Half-Against-Half (HAH) architecture in classification. HAH structure was used with Support Vector Machines (SVMs), k-Nearest Neighbour (k-NN) method and Naïve Bayes (NB) methods. Furthermore, Multinomial Logistic Regression (MNLR) was tested for the dataset. HAH-SVM with the linear kernel achieved clearly the best accuracy being 76.9% which was a good result with the dataset tested. From the other classification methods HAH-k-NN with cityblock metric, HAH-NB and MNLR methods achieved above 60% accuracy. Around 77% accuracy is a good result compared to previous researches with the same dataset.
authors.forename: Henry
authors.initials: H
authors.lastname: Joutsijoki
authors.forename: Kirsi
authors.initials: K
authors.lastname: Varpa
authors.forename: Kati
authors.initials: K
authors.lastname: Iltanen
authors.forename: Martti
authors.initials: M
authors.lastname: Juhola
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D006801:Humans; D015233:Models, Statistical; D009422:Nervous System Diseases / Q000175:diagnosis*; D012044:Regression Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine; D014717:Vertigo / Q000175:diagnosis*